Network-Based Characterization of Depressive Symptoms Among University Students in Lahore with a Focus on Central Drivers of Psychological Distress

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Mubbashar, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8300502/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Depression is a globally prevalent psychological issue with various levels of complications. However, not much data is reported from south Asia and even less from youngsters. We have collected and analyzed data for depression symptoms from various universities in the second biggest metropolitan city of Pakistan. Methods A cross-sectional questionnaire-based survey was conducted by distributing a total of 600 questionnaire among university students from different higher educational institutes. 195 of the responses were later discarded during data normalization process due to several reasons. These questionnaires addressed different depressive indicators including academic workload, routine burden, sleep disturbance, low energy, concentration difficulty, appetite changes, and self-esteem. Network analysis was performed using R-qgraph and bootnet packages. Centrality indices, stability metrics, and edge accuracy were estimated. Descriptive statistics and difference tests were performed. Results Using DSI scoring, it was calculated that a striking 56.5% of the cohort met the threshold for mild depression symptoms, 27.9% fell into moderate symptom category and 13.1% minimal, while only 2.5% reached the severe depression symptoms. Only 9.4% of participants reported psychiatric consultation. Prevalence of depression was higher in female students (43%) than male students (25%). Conclusion Depressive symptoms appear to be a serious concern for university students, and most of this burden seems to come from academic and daily routine pressures. Our analysis shows that workload strain and sleep-related problems sit at the core of these issues and may influence several other symptoms around them. By using network analysis, we can see more clearly which symptoms should be targeted first, allowing universities to design mental-health support that actually fits the needs of students. Depression University students Network analysis Strength centrality Expected Influence Academic stress Mental health Pakistan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The World Health Organization (WHO) has recently reported that more than 1 billion people worldwide are living with some kind of mental health problems [ 1 ]. According to the WHO’s 2025 fact sheet, about 5.7% of adults around the world are living with depression at any given time. This shows that depression remains a common global health concern with a considerable impact on daily life [ 2 ]. Recent epidemiological trends show that the burden of depressive disorders has increased by nearly 13–18% over the last three decades. Much of this rise is linked to population growth and changing demographics around the world [ 3 ]. It is also reported that younger people are facing a higher burden of depressive symptoms. A global meta-analysis reporting more than 1.5 million children and teenagers estimated that almost one in four showed signs of depression. This highlights that adolescents and late-teens, many of whom soon enter university life, are among the most vulnerable groups for developing depressive disorders [ 4 ]. Mental health has long been associated with diet. Students depending largely (40% or more) on ultra-processed foods have been reported to have a lower quality of life and higher mental distress symptoms. Suggesting that eating patterns are a factor in mental health [ 5 ]. Same correlation has been reported amongst students on diet. While on diet they have experienced depression symptoms including stress and anxiety [ 6 ]. Same findings have been confirmed by a systematic review covering 22 studies concerning university students and their increased, uncontrolled, or emotional eating, and/or eating more processed food [ 7 ]. Negative body image is also found to relate to depressive symptoms, in both genders [ 8 ]. Depression has been consistently reported by medical students and university students, worldwide. A meta-analysis covering 130 studies with about 132,000 medical students reported a pooled depression prevalence of ≈ 48% (95% CI: 43–52%) [ 9 ]. A smaller cross-sectional dataset comparing first-year and fifth-year medical students showed that 52.9% of first-year students reported depressive symptoms compared to 35.8% in fifth-year students. Anxiety was also higher in first-year students (35.6% vs 26.3%) [ 10 ]. A large meta-analysis covering 201 studies (≈ 198,000 medical students worldwide, data from January 2020 to April 2022) estimated suicidal ideation at ≈ 15% (95% CI: 11–18%) [ 11 ]. In Thailand, 15.9% of medical students reported suicidal ideation in a 2024 cross-sectional sample (n = 868) [ 12 ]. In China, a national meta-analysis (n = 93,679) reported an overall student depression prevalence of ≈ 34.7% [ 13 ]. Among Sri Lankan university students, PHQ-9 data collected during the pandemic showed a high prevalence of depression, indicating the increased burden faced by students in that period [ 14 ]. Similarly, in Pakistan, during COVID-19, ≈ 48.1% of medical students showed depressive symptoms, and anxiety levels were almost the same (≈ 48.6%) [ 15 ]. According to another study from Pakistan, 9.5% of clinical-year MBBS students experienced suicidal ideation [ 16 ]. An umbrella review, pooling data from 1,655 primary studies and more than 8.7 million university students reported mild depression at ≈ 35.4% (95% CI: 33.9–36.9) and severe depression at ≈ 13.4% (95% CI: 8.0–19.9). Suicide-related outcomes in the same review showed a past-12-month suicidal ideation prevalence of ≈ 10.8% (CI ≈ 9.5–12.1) and lifetime ideation of ≈ 20.3% (CI ≈ 16.2–24.9) [ 17 ]. In Pakistan, pooled data from 7,652 students estimated depressive symptoms at about 42.66% (95% CI: 34.82–50.89%) [ 18 ]. A systematic review of 45 studies found that better diet quality was linked with better mental health in adolescents [ 19 ]. A longitudinal dataset from England among young middle-aged adults showed that frequent consumption of plant-based foods was associated with better emotional well-being and reduced psychological distress [ 20 ]. Findings from Brazil have showed that incoming university students consuming more fresh or minimally processed foods had lower odds of depression, anxiety, and stress compared to those consuming fewer such foods [ 21 ]. Despite the increasing psychological stress reported in university and college students, only a small proportion seek help or receive a formal diagnosis. This indicates a major gap in identifying and supporting students who are struggling. A meta-analysis of 21 studies (2025) reported that only about 28% of students with noticeable psychological problems had reached out for professional help [ 22 ]. First-year medical students showed the same pattern in a 2023 dataset. More than one-third screened positive for anxiety and about one-quarter showed depressive symptoms, but only 7.6% had received any psychological treatment [ 23 ]. Nowadays university students are a major group facing depressive symptoms such as poor diet, and less concentration with mental health services. Many universities have reported increasing psychological problems in student population, while student counseling and depression management strategies are limited. Because of this gap, updated and context-specific evidence is needed to explain how depressive symptoms, diet, academic workload, and help seeking behaviors are connected in university environment. The purpose of this study was to identify these depressive factors in our student group and to provide a clearer evidence base for future prevention efforts and campus-level depression management planning. Methods We have conducted a questionnaire based cross-sectional survey across several universities in Lahore, Pakistan. A total of 600 questionnaires were distributed in different departments, including Allied Health Sciences, Aviation, and medical students of three universities across Lahore. After the screening process, 405 questionnaires were found to be fully completed and were included in the final analysis, and the rest of the incomplete, or otherwise truncated questionnaires were discarded. To measure the severity of depressive symptoms, we devised and used a Depression Severity Index (DSI). This index helped us assign a simple numerical score to each individual sample to help us identify how strongly students were experiencing their symptoms. Using DSI also allowed us to compare severity levels across different domains within the sample. Data Collection A questionnaire was created carrying questions from all three depression related domains, cognitive, somatic, and functional. To build it, we have reviewed earlier epidemiological literature and selected the symptoms that seemed most relevant for our student group. These areas included academic workload, routine pressures, self-esteem, concentration issues, sleep patterns, energy levels, appetite changes, and willingness to seek psychological help. The questionnaire was handed out in classrooms or university premises and were filled under supervision. A brief intro was given, and points were explained when there were any queries. Data Processing We manually screened all the completed questionnaires and then assigned accession numbers. Any responses with unclear or incomplete data were removed from the dataset. Basic information such as age, sex, and academic year was also collected to make comparisons between different subgroups. Network Psychometric Analysis A series of procedures in R were used to better understand the symptoms’ interaction in our study. We used qgraph so an arrangement of the network can be made and we can get a basic view of how the interactions link to one another. We used bootnet package to check the stability of the network arrangement and how precise the edges between nodes were calculated. It gave us a chance to see which symptoms were strongly linked in the overall network. Network Structure We examined the network to identify the symptoms which remained connected even after controlling the shared variance of all other symptoms. In this structure, each node was a symptom and the edges between them represented the stable partial correlation. A stable and reliable link between two symptoms was represented by a thicker edge. This helped us to examine the symptoms which were relatively independent and the symptoms which were influencing each other. Strength Centrality It was used to determine which symptom had the strongest connection with other symptoms within the network. To calculate this strength, absolute values of all edges attached to a node were summed. One of the most central symptoms within the network had higher strength values. Expected Influence We calculated expected influence because strength centrality did not mention if the strong connection of a symptom was positive or negative. This metric was used to measure the direct and indirect impact of a node on others within the network. Expected Influence allowed us to identify the symptoms which were most influential in the network. Centrality Stability We used this network analysis to check the reliability of the centrality results if they remain stable upon removing a small portion of the data. It was done by using the case-dropping bootnet method. We repeated it several times, removed portions of the data to see the change in centrality results. A correlation stability value of 0.5 was set as the minimum acceptable threshold for stable centrality metrics. Edge Stability and Accuracy To check how stable and accurate the edges were, bootstrapping with 95% confidence intervals was produced for every edge weight. The Width of the intervals reflected the consistency of the edges. Difference Tests We applied bootstrapped difference tests to evaluate if the differences between specific edges were meaningful. This step helped us identify which connections or centrality measures reflected actual differences across the bootstrapped samples instead of random noise. Results The final questionnaire comprised 13 open and close ended questions. With 9 questions carrying grading answers and rest were open ended (Supplementary material- Rabia_etal_Questionnaire). A total of 600 questionnaires were distributed among students from different departments of different universities. After strict scrutiny regarding filled data, and the seriousness of student responses, only 405 were deemed fit for further analysis. Data was then entered into excel workbook and responses were converted into numerical values where scale was set as: Strongly disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, strongly agree = 5, for 8 out of 9 close ended questions; except for question about high self-esteem, where values were reversed. Descriptive statistics: Participants’ ages ranged from 17 to 30 years, with the highest participation at 19 (n = 96), 20 (n = 83), and 21 (n = 74) years. Depression Scores Applying the Depression Severity Index (DSI) scoring method, we calculated percentages of the cohort for minimal, mild, moderate, and severe symptoms categories, based on overall scores as well as for the three domains (Table.1). We found that for overall scoring range 9–45, 56.5% of participants scored in the mild category, 27.9% in moderate, 13.1% in minimal, and 2.5% in severe depressive symptom ranges based on total scores. However, patterns differed across symptom domains: Cognitive domain: Mild symptoms were most common (48.1%), minimal symptoms in 33.6%, moderate in 16.8%, and severe in 1.5%. Somatic domain: Mild somatic complaints accounted for 36.0%, moderate for 31.4%, minimal for 24.7%, and severe for 7.9%. Functional domain: Mild impairment appeared in 35.8%, minimal in 29.1%, moderate in 24.4%, and severe in 10.6%. Mild depressive features were predominant, but somatic and functional domains showed higher proportions of moderate and severe symptoms compared to the cognitive domain. Table 1 Distribution of Depression Severity Levels Based on the Depression Severity Index (DSI) Depression Domains Minimal (1–7) Mild (8–10) Moderate (11–13) Severe (14–15) Cognitive 33.6% (136) 48.1% (195) 16.8% (68) 1.5% (6) Somatic 24.7% (100) 36.0% (146) 31.4% (127) 7.9% (32) Functional 29.1% (118) 35.8% (145) 24.4% (99) 10.6% (43) Overall Scale (9–45) 13.1% (9–20) 56.5% (21–30) 27.9% (31–38) 2.5% (39–45) Help-Seeking Behavior Only 38 participants (9.4%) reported a psychiatric consultation, while the majority (90.6%) had not sought any professional help (Table.2). Table 2 Psychiatric Consultation Status Among Participants Consultation Status Frequence (N) Percentage (%) Yes 38 9.38% No 367 90.62% Symptom Responses Heavy academic workload and burdened feeling ware most certified symptoms seen among students.124 students strongly agreed they felt overwhelmed by workload; 90 strongly agreed regarding routine burden. Agreement was also high for low energy (114) and concentration problems (108). Sleep difficulties (82 strongly agreed) and low self-esteem (92 strongly agreed) were common. Dependence on others and high self-esteem showed more disagreement (121 and 100 responses respectively), indicating these symptoms were less central for most students. Table 3 Distribution of Responses for Depression-Related Symptoms (N = 405) Response Category Heavy Workload Burdened in Routine Work High Self Esteem Issues Difficulty in Concentration Dependence on Others Lack of Energy Sleep Deprivation Strongly Agree 124 90 92 75 57 80 82 Agree 86 118 119 108 54 114 89 Neutral 70 85 110 76 73 88 87 Disagree 65 68 53 101 121 65 89 Strongly Disagree 60 44 31 45 100 58 58 Regarding appetite, 150 students reported decreased appetite, 100 reported increased appetite, and 155 reported no change. Table 4 Distribution of Reported Changes in Appetite Change in Appetite Category Frequency (N) Percentage (%) Decreased Appetite 150 37.04 Increased Appetite 100 26.69 No Change 155 36.27 Gender wise analysis Gender differences emerged in symptom endorsement (Table 1 ). Females reported more symptoms overall: 52% of females agreed on heavy academic workload versus 50% of males. 57% of females agreed on burdened routine compared to 41% of males. 47% of females reported self-esteem problems and 45% reported appetite changes. However, males endorsed sleep deprivation more frequently (45%). Highest neutral responses from both genders were reported against high self-esteem, where females counted (39%) as compared to (27%) males. Most of the negative (disagree, strongly disagree) responses were reported by males on many symptoms such as; heavy workload (39%), Burdened routine (36%) and changes in appetite (35%). Table 5 Gender-Based Distribution of Positive, Neutral, and Negative Responses to Major Depression Symptoms Symptoms Female Positive % Female Neutral % Female Negative % Male Positive % Male Neutral % Male Negative % Heavy Workload 52% 23% 25% 50% 11% 39% Burdened in Routine Work 57% 22% 21% 41% 23% 36% High Self-Esteem 47% 39% 14% 43% 27% 30% Sleep Deprivation 40% 24% 36% 45% 25% 30% Change in Appetite 45% 25% 30% 43% 22% 35% Network Analysis of Depressive Symptoms Network Structure The network structure in Fig. 1 represents nodes as symptoms and edges as the partial correlation between the nodes. Thicker edges represent strongest connection between two symptoms such as shown between workload (WL) and burdened routine (BR), and another connection between sleep deprivation (SD) and low energy (LE). Centrality Measures Strength centrality (Fig. 2 .) determines workload and burdened routine as the most connected symptoms in network (strength > 0.75). Sleep deprivation and negative body image strength is moderate (> 0.50). Lowest strength (< 0.25) is shown by self-esteem and Psychiatric consultation. Expected influence (Fig. 3 .) results are closely matching with strength results. Highest score is shown by heavy workload (EI ≈ 0.68) and negative body image (EI ≈ 0.65). These two symptoms are shown to be the strongest drivers in the network. Burdened (EI ≈ 0.60) and low energy (EI ≈ 0.55) scores are moderately high. While sleep deprivation, dependence, concentration difficulty and appetite changes are showing the moderate influence of ≈ 0.50. The lowest EI score is shown by psychiatric consultation (≈ 0.24). EI of self-esteem is 0.00 which suggests that it neither activates other symptoms nor activated by them. This bootstrapped edge-weight analysis (Fig. 4 .) shows the stability and consistency of associations between depressive symptoms. Red line in this plot represents the observed sample estimates and black line shows the bootstrapped mean edge weight. Grey band with 95% interval remains narrow for highly precise edges and weaker edges show wider intervals. Stability Analysis A minimum acceptable threshold for stability metrics was set at 0.5. The results in this case-dropping bootstrap plot (Fig. 5 .) shows the centrality stability coefficient of 0.78. The model is remains stable when half the sample data is removed. It indicates the strong stability of the structure. Edge Differences and Precision This Bootstrapped difference test (Fig. 6 .) shows the stronger edge stability between work load–burdened routine. Grey boxes in this plot indicate nodes or edges that do not differ significantly from one-another and black boxes represent nodes or edges that do differ significantly from one another. Centrality Difference Testing This difference test (Fig. 7 .) confirms that, the central stressors like workload (Strength = 0.89) and burdened routine (Strength = 0.83) are more significant than other symptoms like self-esteem (Strength = 0.21) and consulted psychiatrist (Strength = 0.24). Discussion Depression is one of the most easily neglected mental health issue which is significantly affecting young generation especially university students. We noticed in our study that only 13.1% students were minimal or non-depressed while others showed some of kind of depressive symptoms. More than half of the cohort (56.5%) showed mild depressive symptoms while 30.4% demonstrated the moderate to severe symptoms. Similar percentages were also seen in earlier studies [ 9 , 17 ]. Only a very small portion of participants (9.38%) reported about psychiatric consultation (Table 2 ). In a recent study, somatic symptoms were seen to be more than the cognitive symptoms in students and adolescents [ 24 ]. Our domain level results also show the similar pattern. Most of the moderate and severe symptoms were seen in the somatic and functional domain (Table 1 ). We noticed clear gender differences in our cohort where female students reported higher depression than male students (Table 5 ) and the similar results were also reported in a recent south Asian study. In that study young women reported to be more emotional in academia related stress than their male peers [ 18 ]. Together these evidences support the fact that university students especially females are more sensitive to mental health problems. Most frequent reported symptom by students in our data was academic workload (Table 3 ), confirming many recent studies on university students. Academic workload and competitive environment in universities initiates the depression in students, particularly in LMICs like Pakistan [ 6 , 7 ]. Sleep problems, low energy, concentration difficulties, and constant tiredness were frequently seen in many students. Table 4 showed distribution of participants with changes in appetite. These results were very similar to a study which reported evidence of poor sleep and chronic fatigue as strongly linked stressors. Our monitored appetite changes were also similar to a study which reported altered eating patterns in young adults [ 21 , 9 ]. Our analysis noticed some of the major patterns such as academic workload and feeling burdened (Fig. 1 ). These were the central stressors in the symptom network. These two symptoms were seen to trigger other major symptoms including sleep problems, concentration, and low energy. This finding matches to a recent study which highlighted workload and fatigue as central players for depression [ 25 ]. Some patterns are very clear in our study, focusing on those may improve student mental health as it is also reported in a study from a Chinese college population which showed that targeting these central drivers of depression, like academic workload and sleep disturbances can weaken the symptom network connection [ 26 , 27 ]. Our data clearly showed the central stressors which can become target for therapy to improve overall mental health of students. Reducing academic pressure, promoting healthy sleep, and providing students with stress management can change the environment. This study comes with some limitations like most of the students in this cohort were from province Punjab, it may limit generalizability to other academic cultures. A larger sample size covering university students from all over the Pakistan would support and strengthen future research. This study design was cross-sectional; it could not record data on how these symptoms interact over longer period of time. A longitudinal study is needed to check the symptoms’ progression. Our analysis was tied to specific symptoms. Different questionnaire and additional symptoms might produce alternative network structure. Before applying these findings in any clinical or policy makings, interpret the results cautiously, as the network analysis is sensitive to the symptoms included. Students self-reported all the data so it may be influenced by recall bias or social desirability. Future work can improve and strengthen the validity by incorporating multiple assessment methods. Conclusion This study revealed the central depressive symptoms and domain level patterns in university students. These findings demonstrate many targets for depression management strategies. Given the strong influence of somatic and functional symptoms, along with the central role of heavy workload, negative body image, and low energy, interventions that address these high-impact areas may be particularly effective in reducing overall depressive burden. To truly help students, efforts should focus on easing academic demands, improving sleep, and encouraging early mental health care. Moreover, gender differences shall be taken in account for any interventions and preemptions. Further research will help refine these approaches and guide effective support in universities. Abbreviations DSI Depression Severity Index EI Expected Influence PHQ-9 Patient Health Questionnaire-9 CS-Coefficient Centrality Stability Coefficient CI Confidence Interval LMICs Low- and Middle-Income Countries SRH Self-Rated Health (if cited from comparison studies) Declarations Acknowledgements We are particularly grateful to the support received from all participants in this study. Author Contributions RN conceptualized the study, supervised all of it and proofread the final draft. MA and FG designed the questionnaire. MA, FG, ST and NJ collected the data. FK and MS analyzed the data and produced all results. MA and NJ wrote the initial draft. M prepared the final draft, added results, interpretation and discussed those. Funding This study was conducted without any external financial support or institutional funding. Availability of data and materials The datasets used and analyzed in this study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Ethical approval was received from the Institutional Review Boards at the Superior University, Lahore. All participants gave informed consent before taking part in the study. This study was conducted following the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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BMC Psychiatry. 2025;25(1):334. Li J, Luo C, Liu L, Huang A, Ma Z, Chen Y, Zhao J. Depression, anxiety, and insomnia symptoms among Chinese college students: A network analysis across pandemic stages. J Affect Disord. 2024;356:54–63. Niu C, Jiang Y, Li Y, Wang X, Zhao H, Cheng Z, Li X, Zhang X, Liu Z, Yu X, Peng Y. A network analysis of the heterogeneity and associated risk and protective factors of depression and anxiety among college students. Sci Rep. 2025;15(1):6699. Additional Declarations No competing interests reported. Supplementary Files RabiaetalQuestionaire.docx RabiaetalMastertable.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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07:13:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStrength centrality of depression symptoms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/7ab91860c2a84c6de58a6160.png"},{"id":98377439,"identity":"cd32f74b-f2ce-417c-8b7a-ffeb25cef43d","added_by":"auto","created_at":"2025-12-17 07:13:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpected Influence centrality of 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5","display":"","copyAsset":false,"role":"figure","size":29781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentrality stability plot based on case-dropping\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/5f7f881033de206e96e6b19b.png"},{"id":98440029,"identity":"31a49c03-3efe-4144-9236-d7ede7af4a5a","added_by":"auto","created_at":"2025-12-17 17:03:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":692510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrapped difference matrix for edge weights\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/eb1bf99ba04c72e2fe08aded.png"},{"id":98439234,"identity":"ca853fe8-7b32-4c42-9ec6-b9aa43e052c2","added_by":"auto","created_at":"2025-12-17 17:01:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":584713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrapped Centrality difference test\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/c7a552e017b9fbeedadfb0c7.png"},{"id":104861239,"identity":"ac43c59e-2b7e-4ba1-95f1-d6c4c4102343","added_by":"auto","created_at":"2026-03-18 05:26:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3536657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/2e6fa3d7-ee12-4d62-9cce-299325e8c0fa.pdf"},{"id":98377433,"identity":"422c35d5-c5fa-44cc-9263-a4d049d5bc87","added_by":"auto","created_at":"2025-12-17 07:13:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":71614,"visible":true,"origin":"","legend":"","description":"","filename":"RabiaetalQuestionaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/522b579b2fb462e176997fc5.docx"},{"id":98439810,"identity":"73c1b040-794a-4810-a204-9b54214296b9","added_by":"auto","created_at":"2025-12-17 17:02:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40296,"visible":true,"origin":"","legend":"","description":"","filename":"RabiaetalMastertable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8300502/v1/eb9b3eeff17c974b803294a6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network-Based Characterization of Depressive Symptoms Among University Students in Lahore with a Focus on Central Drivers of Psychological Distress","fulltext":[{"header":"Background","content":"\u003cp\u003eThe World Health Organization (WHO) has recently reported that more than \u003cb\u003e1\u0026nbsp;billion\u003c/b\u003e people worldwide are living with some kind of mental health problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the WHO\u0026rsquo;s 2025 fact sheet, about \u003cb\u003e5.7%\u003c/b\u003e of adults around the world are living with depression at any given time. This shows that depression remains a common global health concern with a considerable impact on daily life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent epidemiological trends show that the burden of depressive disorders has increased by nearly 13\u0026ndash;18% over the last three decades. Much of this rise is linked to population growth and changing demographics around the world [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is also reported that younger people are facing a higher burden of depressive symptoms. A global meta-analysis reporting more than 1.5\u0026nbsp;million children and teenagers estimated that almost one in four showed signs of depression. This highlights that adolescents and late-teens, many of whom soon enter university life, are among the most vulnerable groups for developing depressive disorders [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMental health has long been associated with diet. Students depending largely (40% or more) on ultra-processed foods have been reported to have a lower quality of life and higher mental distress symptoms. Suggesting that eating patterns are a factor in mental health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Same correlation has been reported amongst students on diet. While on diet they have experienced depression symptoms including stress and anxiety [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Same findings have been confirmed by a systematic review covering 22 studies concerning university students and their increased, uncontrolled, or emotional eating, and/or eating more processed food [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Negative body image is also found to relate to depressive symptoms, in both genders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepression has been consistently reported by medical students and university students, worldwide. A meta-analysis covering 130 studies with about 132,000 medical students reported a pooled depression prevalence of \u0026asymp;\u0026thinsp;48% (95% CI: 43\u0026ndash;52%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A smaller cross-sectional dataset comparing first-year and fifth-year medical students showed that 52.9% of first-year students reported depressive symptoms compared to 35.8% in fifth-year students. Anxiety was also higher in first-year students (35.6% vs 26.3%) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A large meta-analysis covering 201 studies (\u0026asymp;\u0026thinsp;198,000 medical students worldwide, data from January 2020 to April 2022) estimated suicidal ideation at \u0026asymp;\u0026thinsp;15% (95% CI: 11\u0026ndash;18%) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Thailand, 15.9% of medical students reported suicidal ideation in a 2024 cross-sectional sample (n\u0026thinsp;=\u0026thinsp;868) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In China, a national meta-analysis (n\u0026thinsp;=\u0026thinsp;93,679) reported an overall student depression prevalence of \u0026asymp;\u0026thinsp;34.7% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among Sri Lankan university students, PHQ-9 data collected during the pandemic showed a high prevalence of depression, indicating the increased burden faced by students in that period [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, in Pakistan, during COVID-19, \u0026asymp; 48.1% of medical students showed depressive symptoms, and anxiety levels were almost the same (\u0026asymp;\u0026thinsp;48.6%) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to another study from Pakistan, 9.5% of clinical-year MBBS students experienced suicidal ideation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An umbrella review, pooling data from 1,655 primary studies and more than 8.7\u0026nbsp;million university students reported mild depression at \u0026asymp;\u0026thinsp;35.4% (95% CI: 33.9\u0026ndash;36.9) and severe depression at \u0026asymp;\u0026thinsp;13.4% (95% CI: 8.0\u0026ndash;19.9). Suicide-related outcomes in the same review showed a past-12-month suicidal ideation prevalence of \u0026asymp;\u0026thinsp;10.8% (CI\u0026thinsp;\u0026asymp;\u0026thinsp;9.5\u0026ndash;12.1) and lifetime ideation of \u0026asymp;\u0026thinsp;20.3% (CI\u0026thinsp;\u0026asymp;\u0026thinsp;16.2\u0026ndash;24.9) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In Pakistan, pooled data from 7,652 students estimated depressive symptoms at about 42.66% (95% CI: 34.82\u0026ndash;50.89%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA systematic review of 45 studies found that better diet quality was linked with better mental health in adolescents [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A longitudinal dataset from England among young middle-aged adults showed that frequent consumption of plant-based foods was associated with better emotional well-being and reduced psychological distress [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Findings from Brazil have showed that incoming university students consuming more fresh or minimally processed foods had lower odds of depression, anxiety, and stress compared to those consuming fewer such foods [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the increasing psychological stress reported in university and college students, only a small proportion seek help or receive a formal diagnosis. This indicates a major gap in identifying and supporting students who are struggling. A meta-analysis of 21 studies (2025) reported that only about 28% of students with noticeable psychological problems had reached out for professional help [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. First-year medical students showed the same pattern in a 2023 dataset. More than one-third screened positive for anxiety and about one-quarter showed depressive symptoms, but only 7.6% had received any psychological treatment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNowadays university students are a major group facing depressive symptoms such as poor diet, and less concentration with mental health services. Many universities have reported increasing psychological problems in student population, while student counseling and depression management strategies are limited. Because of this gap, updated and context-specific evidence is needed to explain how depressive symptoms, diet, academic workload, and help seeking behaviors are connected in university environment. The purpose of this study was to identify these depressive factors in our student group and to provide a clearer evidence base for future prevention efforts and campus-level depression management planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe have conducted a questionnaire based cross-sectional survey across several universities in Lahore, Pakistan. A total of 600 questionnaires were distributed in different departments, including Allied Health Sciences, Aviation, and medical students of three universities across Lahore. After the screening process, 405 questionnaires were found to be fully completed and were included in the final analysis, and the rest of the incomplete, or otherwise truncated questionnaires were discarded.\u003c/p\u003e \u003cp\u003eTo measure the severity of depressive symptoms, we devised and used a Depression Severity Index (DSI). This index helped us assign a simple numerical score to each individual sample to help us identify how strongly students were experiencing their symptoms. Using DSI also allowed us to compare severity levels across different domains within the sample.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eA questionnaire was created carrying questions from all three depression related domains, cognitive, somatic, and functional. To build it, we have reviewed earlier epidemiological literature and selected the symptoms that seemed most relevant for our student group. These areas included academic workload, routine pressures, self-esteem, concentration issues, sleep patterns, energy levels, appetite changes, and willingness to seek psychological help. The questionnaire was handed out in classrooms or university premises and were filled under supervision. A brief intro was given, and points were explained when there were any queries.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eWe manually screened all the completed questionnaires and then assigned accession numbers. Any responses with unclear or incomplete data were removed from the dataset.\u003c/p\u003e \u003cp\u003eBasic information such as age, sex, and academic year was also collected to make comparisons between different subgroups.\u003c/p\u003e\n\u003ch3\u003eNetwork Psychometric Analysis\u003c/h3\u003e\n\u003cp\u003eA series of procedures in R were used to better understand the symptoms\u0026rsquo; interaction in our study. We used qgraph so an arrangement of the network can be made and we can get a basic view of how the interactions link to one another. We used bootnet package to check the stability of the network arrangement and how precise the edges between nodes were calculated. It gave us a chance to see which symptoms were strongly linked in the overall network.\u003c/p\u003e\n\u003ch3\u003eNetwork Structure\u003c/h3\u003e\n\u003cp\u003eWe examined the network to identify the symptoms which remained connected even after controlling the shared variance of all other symptoms. In this structure, each node was a symptom and the edges between them represented the stable partial correlation. A stable and reliable link between two symptoms was represented by a thicker edge. This helped us to examine the symptoms which were relatively independent and the symptoms which were influencing each other.\u003c/p\u003e\n\u003ch3\u003eStrength Centrality\u003c/h3\u003e\n\u003cp\u003eIt was used to determine which symptom had the strongest connection with other symptoms within the network. To calculate this strength, absolute values of all edges attached to a node were summed. One of the most central symptoms within the network had higher strength values.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpected Influence\u003c/h2\u003e \u003cp\u003eWe calculated expected influence because strength centrality did not mention if the strong connection of a symptom was positive or negative. This metric was used to measure the direct and indirect impact of a node on others within the network. Expected Influence allowed us to identify the symptoms which were most influential in the network.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCentrality Stability\u003c/h3\u003e\n\u003cp\u003eWe used this network analysis to check the reliability of the centrality results if they remain stable upon removing a small portion of the data. It was done by using the case-dropping bootnet method. We repeated it several times, removed portions of the data to see the change in centrality results. A correlation stability value of 0.5 was set as the minimum acceptable threshold for stable centrality metrics.\u003c/p\u003e\n\u003ch3\u003eEdge Stability and Accuracy\u003c/h3\u003e\n\u003cp\u003eTo check how stable and accurate the edges were, bootstrapping with 95% confidence intervals was produced for every edge weight. The Width of the intervals reflected the consistency of the edges.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifference Tests\u003c/h2\u003e \u003cp\u003eWe applied bootstrapped difference tests to evaluate if the differences between specific edges were meaningful. This step helped us identify which connections or centrality measures reflected actual differences across the bootstrapped samples instead of random noise.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe final questionnaire comprised 13 open and close ended questions. With 9 questions carrying grading answers and rest were open ended (Supplementary material- Rabia_etal_Questionnaire). A total of 600 questionnaires were distributed among students from different departments of different universities. After strict scrutiny regarding filled data, and the seriousness of student responses, only 405 were deemed fit for further analysis.\u003c/p\u003e \u003cp\u003eData was then entered into excel workbook and responses were converted into numerical values where scale was set as: Strongly disagree\u0026thinsp;=\u0026thinsp;1, Disagree\u0026thinsp;=\u0026thinsp;2, Neutral\u0026thinsp;=\u0026thinsp;3, Agree\u0026thinsp;=\u0026thinsp;4, strongly agree\u0026thinsp;=\u0026thinsp;5, for 8 out of 9 close ended questions; except for question about high self-esteem, where values were reversed.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics:\u003c/h2\u003e \u003cp\u003eParticipants\u0026rsquo; ages ranged from 17 to 30 years, with the highest participation at 19 (n\u0026thinsp;=\u0026thinsp;96), 20 (n\u0026thinsp;=\u0026thinsp;83), and 21 (n\u0026thinsp;=\u0026thinsp;74) years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDepression Scores\u003c/h2\u003e \u003cp\u003eApplying the Depression Severity Index (DSI) scoring method, we calculated percentages of the cohort for minimal, mild, moderate, and severe symptoms categories, based on overall scores as well as for the three domains (Table.1).\u003c/p\u003e \u003cp\u003e We found that for overall scoring range 9\u0026ndash;45, 56.5% of participants scored in the mild category, 27.9% in moderate, 13.1% in minimal, and 2.5% in severe depressive symptom ranges based on total scores.\u003c/p\u003e \u003cp\u003eHowever, patterns differed across symptom domains:\u003c/p\u003e \u003cp\u003eCognitive domain: Mild symptoms were most common (48.1%), minimal symptoms in 33.6%, moderate in 16.8%, and severe in 1.5%.\u003c/p\u003e \u003cp\u003eSomatic domain: Mild somatic complaints accounted for 36.0%, moderate for 31.4%, minimal for 24.7%, and severe for 7.9%.\u003c/p\u003e \u003cp\u003eFunctional domain: Mild impairment appeared in 35.8%, minimal in 29.1%, moderate in 24.4%, and severe in 10.6%.\u003c/p\u003e \u003cp\u003eMild depressive features were predominant, but somatic and functional domains showed higher proportions of moderate and severe symptoms compared to the cognitive domain.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Depression Severity Levels Based on the Depression Severity Index (DSI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression Domains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal (1\u0026ndash;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild (8\u0026ndash;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate (11\u0026ndash;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere (14\u0026ndash;15)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.6% (136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.1% (195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8% (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.5% (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.7% (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.0% (146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.4% (127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.9% (32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.1% (118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.8% (145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.4% (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.6% (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Scale (9\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.1% (9\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.5% (21\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.9% (31\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5% (39\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHelp-Seeking Behavior\u003c/h2\u003e \u003cp\u003eOnly 38 participants (9.4%) reported a psychiatric consultation, while the majority (90.6%) had not sought any professional help (Table.2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychiatric Consultation Status Among Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsultation Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequence (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSymptom Responses\u003c/h2\u003e \u003cp\u003eHeavy academic workload and burdened feeling ware most certified symptoms seen among students.124 students strongly agreed they felt overwhelmed by workload; 90 strongly agreed regarding routine burden. Agreement was also high for low energy (114) and concentration problems (108). Sleep difficulties (82 strongly agreed) and low self-esteem (92 strongly agreed) were common. Dependence on others and high self-esteem showed more disagreement (121 and 100 responses respectively), indicating these symptoms were less central for most students.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Responses for Depression-Related Symptoms (N\u0026thinsp;=\u0026thinsp;405)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy Workload\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBurdened in Routine Work\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Self Esteem Issues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDifficulty in Concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDependence on Others\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLack of Energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSleep Deprivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStrongly Agree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisagree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStrongly Disagree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding appetite, 150 students reported decreased appetite, 100 reported increased appetite, and 155 reported no change.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Reported Changes in Appetite\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange in Appetite Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased Appetite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased Appetite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGender wise analysis\u003c/h2\u003e \u003cp\u003eGender differences emerged in symptom endorsement (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Females reported more symptoms overall: 52% of females agreed on heavy academic workload versus 50% of males. 57% of females agreed on burdened routine compared to 41% of males. 47% of females reported self-esteem problems and 45% reported appetite changes. However, males endorsed sleep deprivation more frequently (45%).\u003c/p\u003e \u003cp\u003eHighest neutral responses from both genders were reported against high self-esteem, where females counted (39%) as compared to (27%) males. Most of the negative (disagree, strongly disagree) responses were reported by males on many symptoms such as; heavy workload (39%), Burdened routine (36%) and changes in appetite (35%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender-Based Distribution of Positive, Neutral, and Negative Responses to Major Depression Symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale Positive %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale Neutral %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale Negative %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMale Positive %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale Neutral %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMale Negative %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy Workload\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBurdened in Routine Work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh Self-Esteem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Deprivation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChange in Appetite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis of Depressive Symptoms\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eNetwork Structure\u003c/h2\u003e \u003cp\u003eThe network structure in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents nodes as symptoms and edges as the partial correlation between the nodes. Thicker edges represent strongest connection between two symptoms such as shown between workload (WL) and burdened routine (BR), and another connection between sleep deprivation (SD) and low energy (LE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCentrality Measures\u003c/h2\u003e \u003cp\u003eStrength centrality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.) determines workload and burdened routine as the most connected symptoms in network (strength\u0026thinsp;\u0026gt;\u0026thinsp;0.75). Sleep deprivation and negative body image strength is moderate (\u0026gt;\u0026thinsp;0.50). Lowest strength (\u0026lt;\u0026thinsp;0.25) is shown by self-esteem and Psychiatric consultation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExpected influence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.) results are closely matching with strength results. Highest score is shown by heavy workload (EI\u0026thinsp;\u0026asymp;\u0026thinsp;0.68) and negative body image (EI\u0026thinsp;\u0026asymp;\u0026thinsp;0.65). These two symptoms are shown to be the strongest drivers in the network. Burdened (EI\u0026thinsp;\u0026asymp;\u0026thinsp;0.60) and low energy (EI\u0026thinsp;\u0026asymp;\u0026thinsp;0.55) scores are moderately high. While sleep deprivation, dependence, concentration difficulty and appetite changes are showing the moderate influence of \u0026asymp;\u0026thinsp;0.50. The lowest EI score is shown by psychiatric consultation (\u0026asymp;\u0026thinsp;0.24). EI of self-esteem is 0.00 which suggests that it neither activates other symptoms nor activated by them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis bootstrapped edge-weight analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.) shows the stability and consistency of associations between depressive symptoms. Red line in this plot represents the observed sample estimates and black line shows the bootstrapped mean edge weight. Grey band with 95% interval remains narrow for highly precise edges and weaker edges show wider intervals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStability Analysis\u003c/h2\u003e \u003cp\u003eA minimum acceptable threshold for stability metrics was set at 0.5. The results in this case-dropping bootstrap plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.) shows the centrality stability coefficient of 0.78. The model is remains stable when half the sample data is removed. It indicates the strong stability of the structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEdge Differences and Precision\u003c/h2\u003e \u003cp\u003eThis Bootstrapped difference test (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.) shows the stronger edge stability between work load\u0026ndash;burdened routine. Grey boxes in this plot indicate nodes or edges that do not differ significantly from one-another and black boxes represent nodes or edges that do differ significantly from one another.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCentrality Difference Testing\u003c/h2\u003e \u003cp\u003eThis difference test (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.) confirms that, the central stressors like workload (Strength\u0026thinsp;=\u0026thinsp;0.89) and burdened routine (Strength\u0026thinsp;=\u0026thinsp;0.83) are more significant than other symptoms like self-esteem (Strength\u0026thinsp;=\u0026thinsp;0.21) and consulted psychiatrist (Strength\u0026thinsp;=\u0026thinsp;0.24).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDepression is one of the most easily neglected mental health issue which is significantly affecting young generation especially university students. We noticed in our study that only 13.1% students were minimal or non-depressed while others showed some of kind of depressive symptoms. More than half of the cohort (56.5%) showed mild depressive symptoms while 30.4% demonstrated the moderate to severe symptoms. Similar percentages were also seen in earlier studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Only a very small portion of participants (9.38%) reported about psychiatric consultation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a recent study, somatic symptoms were seen to be more than the cognitive symptoms in students and adolescents [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our domain level results also show the similar pattern. Most of the moderate and severe symptoms were seen in the somatic and functional domain (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe noticed clear gender differences in our cohort where female students reported higher depression than male students (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and the similar results were also reported in a recent south Asian study. In that study young women reported to be more emotional in academia related stress than their male peers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Together these evidences support the fact that university students especially females are more sensitive to mental health problems.\u003c/p\u003e \u003cp\u003eMost frequent reported symptom by students in our data was academic workload (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), confirming many recent studies on university students. Academic workload and competitive environment in universities initiates the depression in students, particularly in LMICs like Pakistan [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Sleep problems, low energy, concentration difficulties, and constant tiredness were frequently seen in many students. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed distribution of participants with changes in appetite. These results were very similar to a study which reported evidence of poor sleep and chronic fatigue as strongly linked stressors. Our monitored appetite changes were also similar to a study which reported altered eating patterns in young adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis noticed some of the major patterns such as academic workload and feeling burdened (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These were the central stressors in the symptom network. These two symptoms were seen to trigger other major symptoms including sleep problems, concentration, and low energy. This finding matches to a recent study which highlighted workload and fatigue as central players for depression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome patterns are very clear in our study, focusing on those may improve student mental health as it is also reported in a study from a Chinese college population which showed that targeting these central drivers of depression, like academic workload and sleep disturbances can weaken the symptom network connection [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our data clearly showed the central stressors which can become target for therapy to improve overall mental health of students. Reducing academic pressure, promoting healthy sleep, and providing students with stress management can change the environment.\u003c/p\u003e \u003cp\u003eThis study comes with some limitations like most of the students in this cohort were from province Punjab, it may limit generalizability to other academic cultures. A larger sample size covering university students from all over the Pakistan would support and strengthen future research. This study design was cross-sectional; it could not record data on how these symptoms interact over longer period of time. A longitudinal study is needed to check the symptoms\u0026rsquo; progression. Our analysis was tied to specific symptoms. Different questionnaire and additional symptoms might produce alternative network structure.\u003c/p\u003e \u003cp\u003eBefore applying these findings in any clinical or policy makings, interpret the results cautiously, as the network analysis is sensitive to the symptoms included. Students self-reported all the data so it may be influenced by recall bias or social desirability. Future work can improve and strengthen the validity by incorporating multiple assessment methods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed the central depressive symptoms and domain level patterns in university students. These findings demonstrate many targets for depression management strategies. Given the strong influence of somatic and functional symptoms, along with the central role of heavy workload, negative body image, and low energy, interventions that address these high-impact areas may be particularly effective in reducing overall depressive burden. To truly help students, efforts should focus on easing academic demands, improving sleep, and encouraging early mental health care. Moreover, gender differences shall be taken in account for any interventions and preemptions. Further research will help refine these approaches and guide effective support in universities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDSI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepression Severity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected Influence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePHQ-9\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCS-Coefficient\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentrality Stability Coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMICs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and Middle-Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSRH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Rated Health (if cited from comparison studies)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are particularly grateful to the support received from all participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRN conceptualized the study, supervised all of it and proofread the final draft. MA and FG designed the questionnaire. MA, FG, ST and NJ collected the data. FK and MS analyzed the data and produced all results. MA and NJ wrote the initial draft. M prepared the final draft, added results, interpretation and discussed those.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted without any external financial support or institutional funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was received from the Institutional Review Boards at the Superior University, Lahore. All participants gave informed consent before taking part in the study. This study was conducted following the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2025, August 29). \u003cem\u003eDepressive disorder (depression) \u0026ndash; Key facts\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/depression\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/depression\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Sci Rep. 2025;15(1):6699.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depression, University students, Network analysis, Strength centrality, Expected Influence, Academic stress, Mental health, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-8300502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8300502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepression is a globally prevalent psychological issue with various levels of complications. However, not much data is reported from south Asia and even less from youngsters. We have collected and analyzed data for depression symptoms from various universities in the second biggest metropolitan city of Pakistan.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional questionnaire-based survey was conducted by distributing a total of 600 questionnaire among university students from different higher educational institutes. 195 of the responses were later discarded during data normalization process due to several reasons. These questionnaires addressed different depressive indicators including academic workload, routine burden, sleep disturbance, low energy, concentration difficulty, appetite changes, and self-esteem. Network analysis was performed using R-qgraph and bootnet packages. Centrality indices, stability metrics, and edge accuracy were estimated. Descriptive statistics and difference tests were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing DSI scoring, it was calculated that a striking 56.5% of the cohort met the threshold for mild depression symptoms, 27.9% fell into moderate symptom category and 13.1% minimal, while only 2.5% reached the severe depression symptoms. Only 9.4% of participants reported psychiatric consultation. Prevalence of depression was higher in female students (43%) than male students (25%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDepressive symptoms appear to be a serious concern for university students, and most of this burden seems to come from academic and daily routine pressures. Our analysis shows that workload strain and sleep-related problems sit at the core of these issues and may influence several other symptoms around them. By using network analysis, we can see more clearly which symptoms should be targeted first, allowing universities to design mental-health support that actually fits the needs of students.\u003c/p\u003e","manuscriptTitle":"Network-Based Characterization of Depressive Symptoms Among University Students in Lahore with a Focus on Central Drivers of Psychological Distress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:13:03","doi":"10.21203/rs.3.rs-8300502/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f7e3a8b-798a-438f-b3e4-f791208c67b7","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T05:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:13:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8300502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8300502","identity":"rs-8300502","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}